Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data

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Publication Type pre-print
School or College <blank>
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Creator Gerig, Guido
Other Author Durrieman, S.; Pennec, X.; Trouve, A.; Braga, J.; Ayache, N.
Title Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data
Date 2013-01-01
Description This paper proposes an original approach for the statistical analysis of longitudinal shape data. The proposed method allows the characterization of typical growth patterns and subject-specific shape changes in repeated time-series observations of several subjects. This can be seen as the extension of usual longitudinal statistics of scalar measurements to high-dimensional shape or image data. The method is based on the estimation of continuous subject-specific growth trajectories and the comparison of such temporal shape changes across subjects. Differences between growth trajectories are decomposed into morphological deformations, which account for shape changes independent of the time, and time warps, which account for different rates of shape changes over time. Given a longitudinal shape data set, we estimate a mean growth scenario representative of the population, and the variations of this scenario both in terms of shape changes and in terms of change in growth speed. Then, intrinsic statistics are derived in the space of spatiotemporal deformations, which characterize the typical variations in shape and in growth speed within the studied population. They can be used to detect systematic developmental delays across subjects. In the context of neuroscience, we apply this method to analyze the differences in the growth of the hippocampus in children diagnosed with autism, developmental delays and in controls. Result suggest that group differences may be better characterized by a different speed of maturation rather than shape differences at a given age. In the context of anthropology, we assess the differences in the typical growth of the endocranium between chimpanzees and bonobos. We take advantage of this study to show the robustness of the method with respect to change of parameters and perturbation of the age estimates.
Type Text
Publisher Springer
Volume 103
Issue 1
First Page 22
Last Page 59
Language eng
Bibliographic Citation Durrleman, S., Pennec, X., Trouve, A., Braga, J., Gerig, G., & Ayache, N. (2013). Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision (IJCV), 103(1), 22-59.
Rights Management (c) Springer (The original publication is available at www.springerlink.com) ; The final publication is available at Springer via http://link.springer.com/article/10.1007/s11263-012-0592-x#page-2; doi: 10.1007/s11263-012-0592-x
Format Medium application/pdf
Format Extent 5,835,444 bytes
Identifier uspace,18992
ARK ark:/87278/s61c561f
Setname ir_uspace
Date Created 2014-11-06
Date Modified 2014-11-06
ID 712711
Reference URL https://collections.lib.utah.edu/ark:/87278/s61c561f
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